Over the past few years, the term ‘finance transformation’ or ‘financial digital transformation’ has gained traction in the finance sector much the same way as ‘digital transformation’ has. If you play any role in a finance team, you’ll at least have heard of this approach, if not attempted to initiate it in your own organisation.

The problem is that where ‘finance transformation’ once had a clear, targeted meaning, the waters have now become muddied. This means that in place of the expected transparency and optimism around updating the way you work, there is often now a sense of scepticism and anxiety.

Based on our discussions with many CFOs, either they’ve tried it before, with disappointing results from the technologies or provider they worked with, or they’ve heard of negative experiences from peers and want to avoid making the same mistakes.

True finance transformation comes from a combination of process, system and cultural change. It doesn’t make good financial sense to spend thousands on a technology overhaul if your finance team cannot then use that technology. Likewise, a change in your business doesn’t count as finance transformation if your team still feels overworked, stressed and pressured at month end.

A successful finance transformation positively impacts everyone across your business on a day-to-day basis. It lowers your costs and allows you to reduce headcount, doing even more with fewer people. Not only this, but it gives you greater insights into your business, offering easy-to-use data and reporting, ultimately lowering risk to make you more compliant.

With competitors raising their game, now is the time to act.

What is finance transformation

In simple terms, finance transformation, of financial digital transformation, is the combination of process, system and cultural change across the finance operations of a business, which is then implemented through new technologies, training and analysis. As a practice, it is suitable for finance teams seeking to streamline, simplify and optimise their systems through a shift in their approach, to drive strategic value.

Many vendors claim to offer finance transformation, but what this often means is that they’ll sell you the technologies to update your internal finance systems, without the operations training that your team requires. Without enabling strategic change, this often reults is businesses might spend a lot of their budget on the latest technology without being provided with a complete roadmap to transform their systems.

In additiona, it’s easy to be persuaded by ‘quick-win solutions’ which may be more affordable in the short term, but which you will outgrow quickly. CFOs know they need to make changes, but it can feel overwhelming to initiate an “overhaul” when a simpler, more affordable solution might keep problems at bay for a while. These options regularly don’t scale with your company though, and merely tick a box to help you feel like you’re taking steps forward.

Over the long term, what many businesses don’t realise is that they are going to end up spending more money with short term solutions. Proper finance transformation is an initial investment, but over five years the way they are currently doing it works out far more expensive. Sadly, a lot of finance teams work on such a ‘hand to mouth’ way that they don’t often have time to look into better ways of working.

Feeling overwhelmed by your options

Having spoken to many CFOs, we see patterns in the things they tell us: for many, they recognise that their business needs to make a considerable investment but are reluctant to initiate it. Many businesses still rely on convoluted, outdated processes which are potentially damaging to their business – but the alternatives seem too immense to get a grip of.

Being able to predict future outcomes and planning for growth are two activities that often fall by the wayside because completing the current workload feels too all-consuming. This is a regular pattern for many organisations, many CFOs don’t know where they’ll be in the next three to five years, and instead they would like to be able to picture that. To get there, many need help accessing the appropriate technologies that are available and navigating in the right direction for their business.

How financial transformation looks practically

Some practical examples of the changes finance teams can expect from finance transformation include:

• Your finance processes and systems are standardised and automated to avoid error and increase efficiency.
• Your month-end reporting becomes automated, instead of a week or more spent creating a board pack.
• Further automation across business functions will ensure cost-saving opportunities across your department.
• Collaboration across the team becomes far easier: you’re equipped with a centralised finance data hub which enables collaboration.
• Opportunities to operate as a remote team – this means potentially lower wages and more savvy payroll planning.

Understandably, CFOs need a quick, reliable and thorough service. Their team requires training, onboarding, consultation and a ‘people element’ which is often missing from finance transformation offers – it’s a change in attitude as well as a physical process.

Want to read more?  check out our other blogs

How to get the most from your Digital Transformation journey

The growing role of Finance and Accounting in Digital Transformation and business success.

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We have talked about the importance of data and data integration in many of our recent blogs, however we have never really discussed how we do it, so we thought it was about time we did.

Over the last few years, we have been working on a project for one of our mining services clients which involves integrating data from a number of different sources (Excel spreadsheets, Cloud and On-premise applications, Machine/Vehicle Sensor Data etc.) into a centralised data warehouse which is then used for invoicing, budgeting, forecasting, reporting and analytics.

On this project we worked together with another local company, who specialise in data science, AI and machine learning. We have recently formed a closer relationship with this company which enables us, together, to provide our clients with a full End to End Business Operations System (EEBOS). At the core of the EEBOS solution are two distinct tools, Datahub and Mailbot.  These technologies specifically addresses the data integration we are talking about.  This integrated data is then used to drive the reporting, analytics and budgeting/forecasting ‘front- end’ which the user sees and interacts with.

But what exactly is Datahub and Mailbot?

Datahub

DataHub was built from the ground up to seamlessly synchronise data from the various sources and systems. Datahub’s core engine automates record matching and data translations using state-of-the-art algorithms. Data then smoothly flows between synchronised systems to ensure all updates are translated and sent where they need to be. Administrators are kept in the loop with scheduled reports, notifications of edit conflicts via email and the ability to fine-tune the automatically learned translation mappings.

DataHub’s system-specific connectors means adding new systems to synchronise is not a problem cloud-based applications, on-premises systems and real-time (sensor) feeds are all able to be synchronised. Swapping systems from one vendor to another also becomes much smoother since DataHub seamlessly takes care of all the translations and matching, allowing clients to choose best-of-breed systems for each  function of your business and avoid vendor lock-in.

Also, when you need to put together data quickly to keep operations moving, you need to use whatever is at hand – whether it’s Excel spreadsheets, reports or CSV file dumps, you work with what can get you there. But afterwards you’ve got a problem: all these different pieces need to be consolidated back together so that you can summarise and quantify what has actually been happening and determine what needs to be done next.

Need to check the raw data that’s in the data warehouse? DataHub has a fully secure web site that lets authorised users view any data in the system. You can even have it configured so that administrative staff can manage the data directly in the DataHub, letting you store and maintain supporting data that you need but isn’t in your Excel spreadsheets.

DataHub isn’t just a data warehouse, it’s also a complete data integration and synchronisation suite that lets you share data across all your business systems, from third-party software packages to cloud-based apps, corporate data warehouses and even external partner’s IT systems. Make double entry a thing of the past and bring together all your data into a cohesive unit for whole-of-business reporting. It even makes swapping out one vendor’s system for another a seamless experience, so you can choose best-of-breed products for your company.

Mailbot

Most IT solutions expect you to manually convert your data into a format and naming convention that they will accept before allowing bulk import, or worse force you to manually type the data into their product. Wouldn’t it be great if you could instead just email your spreadsheets and reports to a central repository and dashboards and summary reporting? All by nothing more than sending an email with an attachment? Some of you would have heard of the term ’email scraping’.  In essence this is exactly what the MailBot is designed to do.

MailBot is an autonomous data-crunching robot (installed as a Windows service in your IT network) that monitors one or more mailboxes for incoming data in emails (Microsoft Exchange / Office 365 is natively supported). MailBot reads any attachments and automatically processes them, converting and validating the attachment’s contents into structured data and saving it to the DataHub data warehouse. Any dashboards or reports that run off of the data warehouse will immediately pick up the new data – all triggered from a simple email and without a single change to your existing processes.

So how does it work? We know that every company’s data will reflect their unique way of operating, so a one-size-fits-all approach to data processing will never work. MailBot is designed to handle this, being easily extensible with custom connectors tailored specifically to your data formats. Hence there is no need to re-format your spreadsheets.

MailBot will detect errors in data, collate them and automatically send error reports via email to the people who need to know. Fixing the problems and re-emailing in the attachment is all that is necessary to update the DataHub warehouse.

If you make a mistake, just fix it in your spreadsheet and re-email it in – MailBot will automatically notice what is an edit of existing data versus new records and update the warehouse accordingly.

With Datahub and Mailbot you can streamline your process without having to change your process.

To find out more about how Datahub and Mailbot can help your business get in contact with us.

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Many organisations today still use spreadsheets or specialised legacy systems for budgeting and planning; however, these systems were never designed for analytics or reporting.

Those same organisations, if they are using analytics at all, are often relying on their IT to support stand-alone BI solutions.

Today, planning and analytics tools are playing an increasingly critical role in transforming the way organisations gather, store, analyse and interpret data for strategic business decision making.

In the information age we are now living in, managers must   start viewing information as an important strategic resource that if used intelligently will improve their business’s performance and competitive advantage.

The volume, variety, velocity, and veracity of data affecting the organisation’s operations and bottom line continues to increase on daily basis and not all of this data is relevant for strategic decision making.

Data is also dispersed across many ‘point solutions’ that address specific operations of the business. These systems are critical however, to get a complete and overall picture of what is happening in the business, relevant data from these various systems must be integrated into a single source of the truth which is then used for both planning, reporting and analysis.

As an example, we have a client who has a fleet of over 150 trucks and ~300 pieces of equipment and that use sophisticated tracking software and sensors to capture data about routes, speeds, cycle times, etc. A second ‘system’ built into the vehicles which captures engine performance, and a third to measure loads. The financial data on fuel, insurance and other costs associated with the vehicles are yet in a fourth system.

Meaningful data from all these systems needs to be identified, extracted, and integrated into a single complete picture.

The base data used for planning and forecasting, in many cases, is the same as the recent historical actual data used for reporting and analytics.

Managers must be able to identify and separate the relevant data from their systems and find methods on how best to  connect to data from the other systems. The next step in the process is to then “Declutter” by sorting out ‘the clean from the dirt’ and then integrate into a common “Datahub” which can then be used for both planning, reporting and analysis.

Basing decisions on information that is not correct, not current, or not complete is a sure recipe for failure. On the contrary, when the business possesses relevant facts and knows how to use them, improved product innovation, customer relations and operational excellence will be achieved.

Fully knowing and understanding full potential inherent in business planning and analytic tools is the starting point for those companies that strive to repair the broken link between business planning and forecasting and actual performance.

Investments in planning (budgeting and forecasting) and analytic tools if not well researched and planned for can actually cost the company financially, time, resources etc.

As I mentioned above, not all data is important. Remember data collection and storage can be a very costly and time-consuming exercise.

What must you do then to ensure you’re not wasting your time and other resources on non-essential data?

As a manager, you must first establish your company’s competitive parameters within its operating markets and doing so will then help you decide which information to focus on.

To successfully execute your company’s strategies, your strategy should be based on information. In other words, information should be used to as a strategic resource to determine your company strategy.

This means business analytics supporting strategy not only at the functional level but also at the strategic level. In some organisations, business analytics supports strategy performance only at the functional level, monitoring the individual function’s achievement of targets.

There is nothing wrong with this. The problem arises when there is no feedback to the strategic level.

It is therefore important to define targets based on the company’s strategy and the process is made easier if the managers have access to relevant facts that can be used to determine the kind of information relevant for the strategy development and monitoring of performance.

In organisations where business analytics tools give feedback to the strategic level and information is used as a strategic resource, if one department learns to improve its processes through the use of information, the strategy team receives the news and spreads the message throughout the organisation as best practices.

Use of information should both be a bottom-up and top-down process. For example, information should flow down from the top via strategy maps and back up via scorecards and business performance management solutions.

When there is this flow of information up and down, deviations from targets can be measured and analysed and in turn the strategy is adapted and changed to accommodate changes in the market and within the organisation itself.

Dialogue between the strategy and business analytics functions is therefore highly encouraged.

When there is dialogue, there is improved coordination of efforts which can lead to the identification of the organisation’s critical success factors, development of KPIs and the definition of who is responsible for the various KPIs.

Aligning planning and analytics and strategy and in turn linking business analytics and strategy can help provide information about which products create the business’s income over its entire lifetime and information about relevant product attributes to develop for the different customer segments.

Furthermore, investing in business intelligence and analytics and using information as a strategic resource can also provide management with the relevant information and knowledge about which business processes to strengthen and develop in relation to the company’s strategy and competitor’s strengths and activities.

End-to-End Business Operations System for Mining

Why should rolling forecasts become your new normal in budgeting

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Many Australia mining companies have evolved through exploration discoveries, purchase of brownfield operations, rebirth of old operations and the takeovers of ailing operations whose fortunes have fluctuated with the ebbs and flows of their commodity prices. A consequence of this is that for these companies, data gathering technologies have not evolved with the business, and in most case are limited to spreadsheets and stand-alone software packages that require many hours of manual manipulation of information to produce meaningful reports. Coupled with poor control and storage of historical data and it a far from ideal solution for tracking their mining operations.

Most mining businesses have good financial accounting or ERP software in place, due in large part to the universal common structure of finance and accounting. However, when it comes to a mining business’s operations, very few are structured the same. The result of this is there are less, or very few, ready-made out of the box mining operations platforms, which in turn means it is something that generally is not done very well in the mining industry.

Whilst technology is certainly evolving in the industry, many of the solutions are specialist point solutions that address specific areas, for example: drilling, plant management, safety, maintenance, milling, and haulage, where modern mining equipment and vehicles are fitted with sensors to provide performance data, to list a few. Each of these ‘systems’ collect data and report on performance in that specific area, and the do it well. However, to get an overall view of the activities and performance of the company operations, ideally data from all these systems needs to be integrated into a single single source.  The problem is these systems do not easily interface with each other, commonly resulting in large amounts of manual handling and manipulation of data for reporting, invoicing, cost control, budgeting, forecasting etc.

It is in these manual processes where the real hidden costs are. Lack of quality data, repetitive data preparation, and timely availability of data which impact productivity and decision making, are all combining to cost mining companies millions of dollars.

Most of these companies have little appetite for totally changing their systems to a complex and expensive all-encompassing system. As well as the cost, the fear of business interruption, data loss, staff disruption and training, and the stress of running parallel systems in the beginning, can be overwhelming. That is if they can first find the right software package that mirrors their business.

This is the problem the End-to-end Business Operations System (EEBOS) aims to address. Developed by bi5 and Pure Integral, two Perth based companies, this systems fits in with existing systems where required, and can even work with existing spreadsheet based reports and data entry, integrating all of this data through the use of AI, to provide a single source of data on which the full end to end operating process can be monitored and analysed.

bi5 and Pure Integral initially worked together at a well-known mining service company, that is now listed on the ASX. It started life as small construction material supplier of sand, gravel, and crushed stone to the mining industry especially in remote locations. To do this work the business had loaders, road trains, graders, water carts, mobile crushing units and other equipment. Pretty simple and basic activities, however the owner and management of the company decided that technology was going to be their competitive advantage and point of difference so invested in technology right from the start. Whilst they still had the specialist point solutions for the different functions, they focused on automating the integration of data into a single source of the truth for reporting and analysis of the operational and financial performance.

All the performance and load data from the vehicles and equipment is automatically collected and integrated with cost data from Pronto ERP and Payroll systems to provide comprehensive and timely reporting, invoicing both internally and to clients.

The benefits of EEBOS for Mining Operations included:

• Business wide transparency of performance
• Achieve alignment between strategic, financial and operational goals
• Increase, planning, budgeting, forecasting and decision making accuracy and speed
• Automate time consuming and error prone manual tasks
• Drive accountability over results

The first step in our process is to “look under the bonnet” by reviewing all your data capture points. In many cases these are spreadsheets, stand-alone and SAAS software packages. We then trace and map the information flow, collection, and output points. Most importantly we have one on one discussions with all those involved. By doing this we get a good understanding of the problems that need to be solved and we identify your internal clients and what output they require from the data.

We then design a staged workflow that best fits your business. Each stage is designed to stand alone until integrated into the next stage. This methodology provides minimal disruption to staff and systems and is staged to the comfortable capacity of the business. The business is then able to choose when and if it wants to progress the next stage.

Pure Integral and bi5  have now formed a partnership and combined their industry and technical expertise and experience to provide an optional solution to this dilemma. Through well designed, robust, and automated plugin features, smart data bases and Power BI dashboarding we have developed a solution to address this problem and provide a data integration, reporting and analysis and budgeting and forecasting platform with minimal disruption.

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The disruption of business models through the advancement of digital technology has been a reality for other industries for many years. The digital transformation is now impacting the mining industry. From day-to-day data management to the prevention of bottlenecks and inefficiency, information technology is transforming the way the mining industry operates. Increasing demands for efficient production and worker safety are requiring traditional mining companies to embrace innovation and implement information technology to ensure advanced manufacturing and competitiveness throughout the industry.

One of the key issues that hinders data transformation in the mining industry is the fact that most companies have specific point solutions already in place to address the numerous different areas of operations, drilling plant management, safety, maintenance, milling, haulage etc. all with there own data and reporting functions. Most of these organisations have little appetite for totally replacing these systems to a complex and expensive all-encompassing system so they use error-prone manual spreadsheet processes to extract data and cobble together reports for decision making, managment and statutory reporting.

What is reaaly needed is an end to end process which automates the integration function, replacing these manual and repetitive reporting functins with a single source of the truth which provides decision makers with up to date and accurate information they can rely on.

 The following will list three of the main areas that will benefit from the implementation of information technology in the mining industry.

More Data-driven decisions

Mining companies are data-hungry. Information technology can help them locate mineral veins, determine operational risks and streamline operations. It’s no surprise that mining companies are revolutionising how they collect data in the field. With the help of the Internet of Things and advanced software, nearly every device in a modern mine should be “smart” and constantly relaying data such as water pressure, temperature, concentration of gases and other information. With this data, managers can take fast, decisive action to increase efficiency, improve safety and increase the operation’s sustainability.

Process Automation

Mining organisations are realising the benefits of automation in terms of improved productivity, enhanced efficiency and better safety. This translates into greater reliance on technology through remote-controlled equipment and management of mining operations across diverse locations from a central hub.

Mining organisations typically have various systems to manage different sets of data. For mine automation to be effective, it is crucial to ensure systems integration so that all systems work in a synchronous manner to enable seamless data exchange. Since human intervention is minimal, the system should provide automatic alerts and initiate corrective action.

Remote Operations

Connectivity is a challenge that is inherent with mining operations as most sites are located in far-flung areas. This makes it important to ensure remote-controlled operations of the various mining sites through an effective centralized management structure.

Remote operations also translate into lesser on-site team deployment, helping reduce costs and improve employee safety and productivity. At the core of the remote operation is an efficient and reliable communication system – one that ensures seamless channels for interaction between the control hub and various mining sites and enables collaboration between teams at each of these locations.

The Future of Mining

With time, information technology’s predictive and analytical capabilities will improve. Management won’t be pushed out of their decision-making role, but in situations where many variables are at play or time is of the essence, mining companies will likely move operations to algorithms. Data and specifically the ability to organise, manage, process and utilise that data will become a competitive differentiator.

We can also expect to see further developments to make mining more sustainable. Companies will put the power of data toward minimising mining’s negative effects on surrounding ecosystems and communities, moving toward resource extraction with minimal footprint.

Information technology is radically transforming mining, with an eye toward productivity, safety and sustainability. With time, we’ll see even more significant changes.

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Business Intelligence is big, it has been for a while, and there are a large number of platforms out there to choose between.  Decisions on BI implementation, in our view, often fail to recognise the importance of getting it right. Despite what many think it’s not as simple as comparing apples and apples. That is in part thanks to the prevalence of ‘Self-Service’ DIY type Business Intelligence.  So how do you ensure your BI implementation is done right and doesn’t end up costing you more in the long run?  That is through considering the Tech Debt implications.

So first….

What is tech debt

Technical debt can be thought of as a metaphor referring to all the consequences that arise due to poor or compromised development in software.  The consequences are generally with regards to the effort required to improve, enhance, or add additional functionality later on.

Just like with financial debt which we’d all be more familiar with, tech debt incurs interest if it is not repaid, and the longer we take to pay it, or the more debt that is allowed to build the greater the interest.  In a tech sense this interest relates to the amount of effort required to improve or increase functionality later on.

Sometimes real-life metaphors make things easier to understand, so let’s think of your software and tech debt in terms of the car. If you keep delaying getting your car serviced, over time the issues with your car will get more and more. We can keep driving the car with its issues, but it may be doing further damage, and also costing us more to run (much like we can continue with an inefficient process). Alternatively, we could take the car in for a service, but as we’ve waited so long for this service the volume of issues means we will be without it for a few days(much like your systems may be off-line while improvements are made, or functionality is added). We could rent a car that is cheaper to run (much like you can outsource your requirements), or you could go out and trade in for a new car (the same way you can invest in new software).

 3 types of tech debt

Accidental/unavoidable tech debt – Design was flawed and unable to easily add new features quickly/easily – but was not your fault or decision. Using our car metaphor this could be a product recall which was not your issue but does result in you being without your car for a period.

Deliberate tech debt – result of well-considered decisions. Where speed/time/cost is of most importance. Time: startups where time to market is important, or in our world where budget timelines must be met. Cost: where decide to go with the ‘cheap’ option for whatever reason.  This is like buying a small family car because that is all the budget allows, even though you know you will need a bigger car within the next year or two.

Developer tech debt – lack of skills/experience/understanding leading to poorly designed solutions that do not allow for future changes/amendments, changes to businesses etc.  This is like not considering the boot space in the car, and then after purchasing discovering the baby pram won’t fit in the small boot.

How can you limit it?

Accidental and unavoidable tech debt is as the name suggest, unavoidable.

Whilst Deliberate tech debt is a conscious choice, there are things that should be considered to ensure it is not higher than it needs to be.

Just like a family buying a new car, every business has budgetary constraints, so there will always be some element of Deliberate tech debt introduced. Not everyone has the budget to develop everything immediately, to purchase the software they will need in the future now, or to use  the most expensive consultants/developers out there.

So how can you work within these constraints to ensure you are not setting yourself up for further avoidable costs down the track?

In our view there are two elements that contribute to the avoidable components of Tech debt;

1. The choice of platform/software (deliberate), and
2. The choice of consultants/developer (developer skill).

A large part of Tech debt limitation comes down to the choice of platform. Some platforms are easier for the end user, others are easier to develop in. Some are cheaper and faster to ‘go-live’, whereas others are more flexible and are easier to amend/modify down the track.

Every business has different requirements of their software investments, different skills available internally, and difference paths they want to take with their implementation.  Understanding which platform is appropriate for which business is paramount in limiting Tech debt. Having access to consultants and developers who know multiple platforms, and understand the trade-off’s that exist between them, will go a long way to ensuring you select the product.  The product that is right for your business, right for now, and right for the future.

In addition to advising on the platform and product choice, consultants and developers are the main determinant of Developer tech debt. As we have been discussing, like all debt, tech debt is a long-term issue, and it can increase significantly over time if it is not managed.  Because of this it is important to ensure your consultants have not only the skills to develop within the chosen platform, but also the skills to understand your future use case, and where your business may be in 3, 5, or 10 years.

Consultants who take a longer-term view of the development and are focussed on ensuring their solutions deliver value well into the future will help to limit your tech debt. We believe consultants with both an understanding of the technical and financial/operational are perfectly positioned to provide this.  The financial and operational understanding allows them to easily understand your business, its processes and operations, and understand where it may be headed and how that will impact on the solution developed.  The can then easily translate this to the technical requirements of the solution, and incorporate these requirements back into the platform/software selection process.

Why does it matter?

For most of our readers and Finance professionals, getting into how to identify, value and manage Tech debt might be a bit too deep. Simply being aware of how you might introduce tech debt into your business, the future consequences of this, and how to limit it is what is most important.  However if you do with to delve further into Tech Debt (and the flip side Tech Equity, there is a very good article by McKinsey on exactly this.

But if Tech debt is ‘tech’ and not financial, why does it matter?

Because it costs money to reduce Tech debt, and it may cost your business if you don’t.

As businesses change in size and scale so do their requirements. The easiest way to understand is to think of accounting or ERP systems. Whereas once a small ERP system might have been appropriate, as the complexity and size of the business increases this ERP system may no longer be fit for purpose. Trying to make it work can require significant manual intervention, which often leads to data errors, resulting in reporting delays, and overall process inefficiencies.  This often results in a decision to change ERP’s.  Depending on the change this can be simple, or more often than not be a very slow and difficult process, with a significant cost involved, both for new licensing and the implementation.

Whilst we are not suggesting every business should start with the biggest and best ERP system, when selecting the platform, it is important to understand what you may require in the future, and determine how difficult, and expensive, it will be to add that functionality to the chosen platform in the future.

What about Tech Debt in Business Intelligence..

Business Intelligence is essentially visuals, backward looking analytics. It is not an ERP as used in the example above, or a car, so how does Tech debt relate to a Business Intelligence implementation?  In exactly the same way.

There are multiple Business Intelligence platforms out there, and nearly all of them will do what you want, but ensuring you select the best platform and implement in the correct way is still important in limiting your tech debt.  Anything can be changed down the track, but how hard will that be and how much will that cost?

When we get approached by prospective clients about implementing a Business Intelligence solution, the initial discussion is often around what they want now.  Often People are putting Business Intelligence solutions in place to meet an immediate need, without properly considering if that will be suitable later on.

BI is analytics, and in analytics the structure of the data is key to ensuring the required analytics are available. So having a consultant who can ask the right questions to get you to consider HOW you may want to analyse in the future is important.

Some things that should be considered are;

• Changes to business structure and reporting levels. Eg. whilst you may only have 2 divisions now, how many may you have in the future, will there be sub-divisions underneath these?
• Current ERP and likelihood of it changing. The ERP can significantly impact the options available to get data into your BI platform, which may then limit your choice of BI platform.
• Frequency of data updates. The frequency with which you want your BI analytics data to update is an important consideration in both the platform selection and data structuring.
• Chart of Accounts structure.  The Structure of your CoA (or lack of one) can determine how the data needs to be structured in your Business Intelligence solution, and how much ‘manual’ control you will need, to generate the reporting you require
• Dashboard and report commentary entry. Not all BI platforms allow for data entry, so you need to ensure the right platforms, or platforms, are selected.
• Self-service requirements. Do you want everyone to build their own dashboards, or do you want a central control which develops for release to the business.
• What-if/sensitivity analysis and Budgeting and Forecasting requirements. This is only possible in certain BI platforms.
• Users and access. The way in which you want to distribute dashboards and reports across your business, and the functionality you want in these will play a part in determining the appropriate platform

This is far from an exhaustive list, but hopefully it has given you a starting point for what you need to consider when selecting and implementing your Business Intelligence solution.

Related Articles

Four factors that influence BI success

7 Steps to get meaningful insights from your data

The post How does tech debt impact a Business Intelligence Implementation appeared first on bi5.

November 2020

Australia is home of some of the most precious metals in the earth. Highly specialised processes and equipment are often required to extract, refine and use these precious resources. As technology evolves and becomes more sophisticated our ability to tap into these resources is improving all the time, allowing previously untapped deposits to become accessible and viable for production. 

In the world of digital technology there is a focus on extracting and using another valuable resource – ‘data’. In the digital world, ‘data’ is the new gold, ‘meaningful insights’ from data is the new platinum and ‘actionable insights’ are the most valuable of resources. Mirroring the mining world, as businesses become more sophisticated at tapping into data, they are able to go deeper and extract more valuable and actionable insights.

All well and good you say, but what does this talk of resources have to do with the CFO? Well, when it comes down to it, money is the most critical resource in any business – and who controls the money? The CFO. 

The new role of the CFO

A CFO’s traditional role has been to focus on strategy and the end-state aspects of financials. But in today’s data-rich environment, CFOs are expected to understand and use it to predict, influence and shape decisions that drive financial results.

When a business can extract meaningful insights from data, it has the knowledge to become more competitive, tap into new markets, better develop products and services, provide a better customer experience and generally increase profits.

But just like mined ore, data alone is of no particular value, it must be processed and shaped into something more ‘actionable’ before it can add value to a business. This transformation is often wrongly termed as big data, Internet of Things, predictive analytics, machine learning and artificial intelligence. A more correct reference is ‘integrated data’. 

Every business has a plethora of specialist systems that collect data from various operations and many external sources.  The data from these sources needs to be integrated, validated and current in order to get a complete and accurate picture of the financial health of a business. Companies are turning to their C-level people, specifically the CFO, to lead this operation.

So how can modern CFOs turn data into actionable insights? Here are our 7 steps to get the right data alchemy.

Step 1: Identify key success drivers

Based on our experience, there are typically 15 to 20 non-financial metrics which are drivers of enterprise performance. CFOs need to find what these performance drivers are and then ensure that the right data is continually collected, shared and used. If you are unsure or just starting your data analytics journey, use this as a starting point and progress from there.

By way of example, one of our mining contractor clients that supplies mobile crushing and haulage services to remote mine sites determined two of the most important drivers of success are cost per tonne and cost per kilometre.  When the client decided to collect and post the data daily on these two metrics to the enterprise dashboard, the enterprise’s focus completely changed.

Step 2: Look for relevant data everywhere  

Data is everywhere. CFOs must step away from their financial data lens and start to survey and analyse the different data that exists outside the chart of accounts. 

CFOs must also identify which data sources are essential for the success of their organisation. For instance, the requirements for a project-based business might require in-depth insights into every project, past and present. On the other hand, a mining company would be more interested in data that helps analyse daily production results and costs.

Most importantly, CFOs must have the ability to cross-compare multiple sources of micro and macro-level data to identify and understand correlation and causation.

Step 3: Clean your data

Unfortunately, unless it is collected by systems without any human intervention, enterprises have to live with “dirty data.”

We’ve all heard the saying “garbage in, garbage out, so it’s important we know the information is right ”  So, whether data has not been consistently recorded, is missing or hasn’t been maintained, it will likely need some refining before it can be trusted for use.

One small but critical change is to make the names of fields understandable and recognisable. Data stored in ERPs and other systems has naming conventions that will mean nothing to business users, or any users for that matter.

A disciplined approach must be used for ensuring data is scrubbed clean and is consistent across different systems. Otherwise, information and insights will be derived from misleading and incorrect information.

Step 4: Integrate data

Very few companies have all their data available in one system. We find Internal data often exists in multiple business systems and external data often arrives piecemeal. This “disconnected” data means we might have to deal with “islands of data”.

It is becoming more viable and more common for companies to create their own centralised data warehouse/data marts/data lakes where data from various systems is extracted, transformed, loaded and then linked and analysed.

CFOs have the responsibility to identify islands of data and initiate the centralisation of data for use.

Step 5: Upgrade your data visualisation

Data by itself is essentially unreadable. It has to be refined, packaged and viewable from every angle for data analysis to shine with insights.

Many companies use Excel for visualisation of data at the cost of many administrative labour hours. 

Don’t get me wrong, Excel is an excellent tool when used for what it is designed for – a personal productivity tool. Sure it can do a lot of ‘good stuff’, but this usually has to be driven by the business’s Excel guru – which that’s fine if you can chain them to the desk and they stay with your organisation indefinitely, but not really practical if you’ve got multiple people needing to make sense of information at the same time. Excel is not a scalable or sustainable solution for data delivery.

It is the CFO’s role to invest in data visualisation or business intelligence tools that are able to provide an intuitive display of historical information, and which also allow for sophisticated data analysis that will help drive strategy and action for the future.

Step 6: Drive accountability for data usage

When a business makes a significant investment in its data (as per the previous five steps), it is the responsibility of the decision-makers to use this knowledge. I hear so many stories of key executives who are reluctant to ‘take Fred’s spreadsheet away from him’ –  a classic case of the tail wagging the dog. This does not make sense.

Like any other investment, CFOs need to monitor the usage of this “data-to-insights” investment by the key decision-making people in the business. 

I typically recommend that companies start using their enterprise dashboards (data) as a wall of fame for leaders and provide full transparency by kicking off monthly, quarterly and annual performance meetings with the dashboards displayed front and centre for everyone to see. Then the business decision-makers can use the dashboards to elaborate on insights and action that helped move the needle.

The only way to convert your data into results is to make sure leaders and decision-makers are making the most from it.

Step 7: Ensure security and governance

As a valuable resource, data must be protected and governed. There are immense benefits to becoming a data-driven enterprise, but there are also risks:

Data validity and ethics – ensure only valid data is used and reasonable insights are inferred.  CFOs need to outline strict and monitored data collection and storage and interpretation methods, and ensure that all involved are held accountable to the highest standards of ethical behaviour.

Data security – prioritise data protection and privacy. Like any other precious resource, data is vulnerable to theft. When an enterprise starts to collect and mine data, there’s a risk that it may be breached or accessed by external entities that exploit it for their benefit. A CEO should take the necessary action to be able to ensure data security.

Like any other precious resource, data is vulnerable to theft. When an enterprise starts to collect and mine data, there’s a risk that the data can be breached or accessed by external entities that exploit it for their benefit.

Final thoughts

Data-driven, high-performance companies require CFOs to be on top of understanding the business model and performance/value drivers of the organisation (which can’t be found in the chart of accounts or financial statements). It requires investment in not just data collection systems, but analytics and visualisation platforms.

CFOs are stepping up to lead the data-to-insight revolution and work with other business leaders to build a solid foundation of data mining, refining and reporting that can transform already valuable information into actionable insight to drive better decision making.

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June 2020.

One of the hardest issues we face at bi5 is explaining what we do. Not because we don’t know what we do, but because it can be difficult to keep it short and explain it in a way that most people would understand.

At different times, and depending on the person we’re speaking to, our explanation will include;

• budgeting and forecasting systems
• Data Analytics
• Business Intelligence and visualisation, or the more general
• “helping businesses make better use of their data”.

While they all seem like different things, there is one common element to all of them, and that is data, data warehousing and data mining.

While none of my responses mentioned data warehousing and data mining, without them we wouldn’t be able to deliver the required solutions and provide the benefits we do to our clients.

What is Data Mining and Warehousing?

So what do Data Warehousing and Data Mining mean, and why are they important to businesses and CFO’s when they sound like something that would fall under the CIO’s realm?

Data warehousing is essentially the integration of data from disparate sources (generally transactional systems such as ERP, CRM, sales, inventory management, payroll) into a single ‘warehouse’, or database schema. The design of this schema/warehouse is important as it provides the basis for your query and analysis functions.

Data mining on the other hand is the utilisation of this data, the mining of the data within the data warehouse. Through Data mining businesses try to find hidden information, to recognise patterns in their data coming from different systems they weren’t aware of, or which perhaps they were aware of but did not fully realise the financial impact.  Data mining can take many forms and use different methods such as AI and machine learning, but they still just provide a choice. The ultimate decision must still be made by a person.

Why are Data Warehousing and Data Mining important?

You would have a good idea about how your business operates, and what the correlations are between different areas within your business, and between your business and macro economic conditions. But how do you quantify these. As useful as a gut feel is, sometimes it helps to have data to underpin your assertions and estimations. That is where data warehousing and data mining can help.

Through integrating your data within a warehouse, and then mining it you can see how performance in one area impacts another.  If you don’t have enough stock then you know you will have difficulty in filling orders, but what is the impact of that?  Does it just delay payment as delivery is pushed out, or does it actually result in lost or cancelled sales? Through tracking individual customers you could also see if there is any impact on the value of future orders for those who have experienced a delay in delivery arising from poor stock management.

We’ve had discussion with several engineering and construction businesses where their ability to get a consolidated view of performance  across all their projects is limited. With the use of a data warehouse to integrate their data they can start to get a better whole of business picture. Rather than only getting a consolidated bottom line, or each project individually, they can start look at consolidated performance by other dimensions. That could be by project scale, project type, or activity type.  Perhaps on a consolidated level everything looks fine, but if you had the ability to look at individual activities on a consolidated basis you would notice that there is a consistent overspend on a particular activity, or to a particular contractor or supplier, which would warrant further investigation.

So can you and your business benefit from mining and warehousing

The word warehouse would have many people thinking of a physical warehouse, maybe at a data centre, but it can be as simple as a new piece of software or database.

At bi5 we use a few different technologies to provide our clients with the benefits of data warehousing and data mining.  Products such as BOARD, Jedox, and to a lesser extent Power BI provide the data integration capabilities that are needed in a data warehouse. The allow you to integrate data from multiple sources and integrate it together into your own database. They also provide business intelligence functionality within the same platform to provide you with the ability to mine this data to notice patterns that exist within your business.

Products like BOARD and Jedox also take this one step further and allow you to ‘play’ with these patterns through some what-ifs analysis. This is where you can use scenario planning and sensitivity analysis on the patterns/relationships you’ve noticed to help you make the right decision for your business.

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While motivations vary, businesses of all sizes, industries, and geographies are turning to cloud services. Whether you’re looking at Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS), or Platform-as-a-Service (PaaS), the predictions are the same: fast growth of the workloads placed in the cloud and an increased percentage of the total IT budget going toward cloud business solutions.

Many factors are driving cloud adoption, according to a study conducted by the market research company Vanson Bourne. “The Business Impact of the Cloud” report, which contains interviews of 460 senior decision-makers within the finance functions of various enterprises. The report named 11 drivers of cloud adoption along with measurable improvements companies have achieved by installing cloud business solutions to achieve productivity, lower cost, and improve time to market.

The cloud is a great way to run a business, since it offers many advantages and only a few disadvantages. Here are 3 key benefits an enterprise can expect to achieve when adopting cloud solutions for their business intelligence and budgeting and forecasting needs.

Scalability with cloud solutions

Different companies have different IT needs, a large enterprise of 1000+ employees won’t have the same IT requirements as small to medium-sized business. Using a cloud solution is a great way to enable the enterprise to efficiently and quickly – scale up/down their IT activities, according to business demands.

Cloud based solutions are ideal for businesses with growing or fluctuating bandwidth demands. If your business demands increase, you can easily increase your cloud capacity without having to invest in physical infrastructure. This level of agility can give businesses using cloud computing a real advantage over competitors.

This scalability minimises the risks associated with in-house operational issues and maintenance, which are of paramount importance in the business intelligence and data analytic area, where timeliness if key. You have high-performance resources at your disposal with professional solutions and zero up-front investment. Scalability is probably the greatest advantage of the cloud.

Portability–Remote IT Infrastructure with cloud solution

To touch on flexibility further, we must include how the cloud offers near limitless computer resources to its users regardless of their location. This is one of the major difficulties faced by many businesses in their budgeting and forecasting solutions, where those involved in the process are often based in different offices, cities and countries.

The ability to allocate an entire server-farm’s equivalent of computer power for any project or task via a few simple clicks is undeniably appealing. Every major player in business uses this kind of platform to provide its users and internal employees access to their secured data for their analytical requirements where-ever they are located.

Do more with less

With cloud business solutions, companies can reduce the size of their own data centres — or eliminate their data centre footprint altogether. The reduction of the numbers of servers, the software cost, and the number of staff can significantly reduce IT costs without impacting an organization’s IT capabilities.

Conclusion

Cloud computing adoption for business intelligence and budgeting and forecasting is on the rise every year, and it doesn’t take long to see why.  Enterprises recognise the benefits of cloud solutions and see how they impact their production, collaboration, security and revenue. Whether you intend to use the public cloud, a private cloud, or a hybrid model, cloud based business solutions can provide many layers of benefits to your business.

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In today’s professional environment the management of massive data sets presents a challenge to many businesses. Big data refers to data sets that are too large and complex for traditional data processing and data management applications. On a deeper level, big data is distinguished by its volume, velocity, and variety.

By gathering, analysing, and using massive amounts of digital information, organisations can improve business operations. As data sets continue to grow, and applications continue to become more real-time, big data processing applications require the right capacity to produce actionable insights.

Why Big Data needs a Business Intelligence application

Whether you are working in the manufacturing sector or service sector, your organisation relies on data. Data is the heart of business decision-making and will continue to influence decision making process. Organisational data is often stored in different, separate systems. These systems do not communicate with each other.

“Clean data is better than big data” is a common phrase among experienced data science professionals. Having tremendous amounts of data will not give you a competitive advantage over your competitors, but how effectively and quickly you analyse the data and extract actionable information from it will. Business Intelligence tools combining data from different operations and show how information relates to each other.

Integrating business intelligence for big data is an important step toward gaining full return on investment. Business intelligence tools can be highly complementary; the system provides a deeper, exploratory perspective on the data, additionally it provides a more structured user experience. The capabilities of business intelligence applications in dashboard visualisation, reporting, performance management metrics, and more can be vital to making advanced analytics actionable.

The future of Big Data and Business Intelligence

It’s more than obvious how much of an impact big data and business intelligence have on how a business operates, regardless of the size or what industry the business is in.

We can agree that big data has taken the business world by storm. So, what does the future hold for Big Data and business intelligence? Let’s look at one of the most popular trend big data is facing.

• Big Data moves to the cloud

Big data has benefits but in order to utilize big data, enterprises have to be able to store and process it. State of the Cloud Survey pointed out that 95% of the respondents are using cloud services. 89% use a public cloud, 72% use a private cloud and 67% use a hybrid solution. Big data applications are divided into three broad categories: storage, processing, and development. Many big data clouds are configured to support a combination of two or all three types.

Final Thoughts

Companies that inject data and analytics deep into their operations, can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition. The increasing landscape of data-driven businesses, who encourage greater transparency operations, better predictions and analyses, and faster testing through big data utilisation will be able to set themselves apart from the rest of the field.

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